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    Nonparametric Estimation of Functional Dynamic Factor Model

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    Type
    Preprint
    Authors
    Martinez Hernandez, Israel cc
    Gonzalo, Jesús
    González-Farías, Graciela
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-11-03
    Permanent link to this record
    http://hdl.handle.net/10754/665877
    
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    Abstract
    For many phenomena, data are collected on a large scale, resulting in high-dimensional and high-frequency data. In this context, functional data analysis (FDA) is attracting interest. FDA deals with data that are defined on an intrinsically infinite-dimensional space. These data are called functional data. However, the infinite-dimensional data might be driven by a small number of latent variables. Hence, factor models are relevant for functional data. In this paper, we study functional factor models for time-dependent functional data. We propose nonparametric estimators under stationary and nonstationary processes. We obtain estimators that consider the time-dependence property. Specifically, we use the information contained on the covariances at different lags. We show that the proposed estimators are consistent. Through Monte Carlo simulations, we find that our methodology outperforms the common estimators based on functional principal components. We also apply our methodology to monthly yield curves. In general, the suitable integration of time-dependent information improves the estimation of the latent factors.
    Sponsors
    This research was partially supported by 1) CONACYT, Mexico, scholarship as visiting research student, 2) CONACYT, Mexico, CB-2015-01-252996 and 3) Centro de Investigaci´on en Matem´aticas (CIMAT).
    Publisher
    arXiv
    arXiv
    2011.01831
    Additional Links
    https://arxiv.org/pdf/2011.01831
    Collections
    Preprints; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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